This　article　studies　privacy-preserving　collaborative　learning　in　decentralized　Internet-of-Things　(IoT)　networks,　where　the　agents　exchange　information　constantly　to　improve　the　learnability,　and　meanwhile　make　the　privacy　of　agents　protected　during　communications.　However,　the　harsh　constraints　in　IoT　make　executing　collaborative　learning　much　more　difficult　than　well-connected　systems　composed　by　servers　with　strong　computation　power,　due　to　the　weak　capacity　of　devices,　limited　bandwidth　for　exchanging　information,　the　asynchronous　communication　environment,　and　the　necessity　of　privacy　preserving.　We　show　that　even　if　with　the　harsh　constraints　in　IoT,　it　still　can　devise　efficient　privacy-preserving　collaborative　learning　algorithms,　by　proposing　the　first　known　decentralized　collaborative　learning　algorithm　for　the　fundamental　multiarmed　bandits　problem　under　the　framework　of　local　differential　privacy.　Rigorous　analysis　shows　that　the　proposed　learning　algorithm　can　make　every　agent　learn　the　best　arm　with　a　high　probability　and　keep　the　privacy　preserved　meanwhile.　Extensive　experiments　illustrate　that　our　learning　algorithm　performs　well　in　real　settings.